We express the mean and variance terms in a double-exponential regression model as additive functions of the predictors and use Bayesian variable selection to determine which predictors enter the ...

Interpretability has drawn increasing attention in machine learning. Partially linear additive models provide an attractive middle ground between the simplicity of generalized linear model and the ...

We propose a two-step estimating procedure for generalized additive partially linear models with clustered data using estimating equations. Our proposed method applies to the case that the number of ...

The first edition of this book has established itself as one of the leading references on generalized additive models (GAMs), and the only book on the topic to be introductory in nature with a wealth ...

"Generalized Linear Models," Tuesday, April 6: The third LISA mini course for the 2010 Spring semester focuses on appropriate model building using generalized linear models. While multiple linear ...

Ordinary linear regression (OLR) assumes that response variables are continuous. Generalized Linear Models (GLMs) provide an extension to OLR since response variables can be continuous or discrete ...

Genome-wide association studies (GWAS) are widely used to uncover novel genetic susceptibility loci for complex genetic diseases. If the true genetic model is known, the association test reflecting ...

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Keywords: Statistical analyses. Regression models. Post-earthquake ignitions. Data analyses. California. Ground shaking. Generalized linear mixed models. Goodness-of ...